@phdthesis{digilib78918,
           month = {July},
           title = {KLASIFIKASI PENYAKIT PNEUMONIA DENGAN MODEL FUSI CITRA MULTIMODAL MENGGUNAKAN PENDEKATAN DEMPSTER-SHAFER THEORY},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22106050023 Hernadhif Rafif Wiryawan},
            year = {2026},
            note = {Dr. Siti Mutmainah, S.Kom, M.Cs.},
        keywords = {pneumonia; multimodal imaging; evidential neural network;  Demspter-Shafer Theory},
             url = {https://digilib.uin-suka.ac.id/id/eprint/78918/},
        abstract = {Pneumonia is a lung infection that ranks among the leading causes of death in children under five, with mortality rates continuing to rise as children get older. This study proposes a pneumonia classification model based on multimodal imaging that integrates an Evidential Neural Network (ENN) based on the Dempster-Shafer Theory (DST) framework. The model was built through five main stages: data collection, preprocessing, feature extraction using ResNet-34, followed by training the ENN model independently for each modality, and concluding with the combination of mass functions using Dempster?s rule of combination. Model evaluation was conducted using the}
}